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Surface And VOlumetric Registration (SAVOR)
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Constructing a one to one correspondence between whole brain MR image scans is a problem of critical importance in neuroimaging analyses. We have developed a framework to combine the strength of both surface-based and volumetric-based analyses for consistent, bijective data transfer between brain coordinate systems.
By combining a good volumetric registration, with a topology preserving projection from one surface to the other, anatomical surfaces can be registered accurately. SAVOR yields registrations with high correlation of cortical biomarkers and little misregitration of cortical parcellation labels.
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Highly-Automated Interactive Medical Image Segmentation
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In order to produce accurate segmentations of 2D and 3D structures, manual intervention is often unavoidable. We are working on techniques that allow the user to provide minimal intuitive interaction for guiding the segmentation.
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Groupwise Medial Axis Transform
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We augment the traditional medial axis transform with an additional coordinate stored at each medial locus, indicating the confidence that the branch on which that locus lies represents signal and not noise. This confidence is calculated based on the support given to that branch by corresponding branches in other skeletons in the group. This method is used to produce a fuzzy skeleton and to perform intelligent pruning
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Motion Correction in Medical Imaging
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Positron emission tomography (PET), functional Magnetic Resonance Imaging (fMRI), and other functional medical imaging modalities are used to assess brain function in normal and
disease states, but, in general, all are susceptible to head movement. We developed a method for tracking head pose that eliminates the tracker dependence on attaching markers to the head. In particular, a stereo video tracking system, in which left and right high resolution video cameras record head movement and computer vision methods calculate the head’s 3D position, is used.
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VascuSynth: Simulation of Branching Tubular Structures for Validation and Learning
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Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed algorithms. We are developing a method for simulate volumetric images of vascular trees with the corresponding ground truth segmentations, bifurcation locations, and tree hierarchy.
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Simulation of Ground Truth Data
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The problem of scarcity of ground-truth, expert annotated
medical image data is a serious one that impedes the training and validation of
medical image analysis techniques. We develop algorithms for the automatic
generation of large databases of annotated images from a single reference
dataset and provide a web-based interface through which the users can upload a
reference data set and download an arbitrary numbers of novel ground-
truth data.
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SMRFI: Shape Matching via Registration of Feature Images
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We perform shape matching by transforming the problem into an image registration task. At each vertex on the shape, we calculate a shape feature and encode this feature as image intensity at appropriate positions in the image domain. Calculating multiple features at each vertex and encoding them into the image domain results in a vector-valued feature image. Establishing point correspondence between two shapes is thereafter treated as a registration problem of two vector-valued feature images. With this shape representation, various existing image registration strategies can now be easily applied. These include the use of a scale-space approach to diffuse the shape features, a coarse-to-fine registration scheme, and various deformable registration algorithms.
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MR Neurography of the Sciatic Nerve
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This project focuses on the study of the sciatic nerve through MR neurography. It has focussed on the development of MR protocols for imaging uninjured peripheral nerves, and the construction of computational measurement techniques for several key characteristic features of nerves. Work has also been done to create visualization tools based on rapid prototyping technologies.
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3D Shape Descriptors for Human Peripheral Nerves
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This project is a collaboration with Dr. Andy Hoffer of the SFU School of Kinesiology.
We are working on developing shape descriptors for peripheral nerves. One way to describe them are through their skeletons or centerlines. Skeletons are useful representations for nerves as they contain most of the information that one would want like length and the number of bifurcations. Various skeletonization programs were researched and tested to find how good they are when applied to our datasets. A suitable program that uses Voronoi diagrams to get the medial axis was found and it was run using the isosurfaces of the nerve objects.
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Shape Matching using Ant Colony Optimization
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We have developed the first Ant Colony Optimization algorithm specifically aimed at solving the Quadratic Assignment Problem for establishing shape-correspondence, with proximity information incorporated.
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Image Crawlers
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Image Crawlers, a new breed of Deformable Organisms, are equiped with 3D tubular medial-based bodies, a new repertoire of sensory modules (e.g. Hessain-based, hemispherical sensors), behavioral routines (e.g. grow, spawn children branch cralwers), and decision making strategies (e.g. branch detection, growth direction). They crawl along tubular and tree-like structures in medical images, segmenting boundaries, detecting
and exploring bifurcations, and providing sophisticated,
clinically-relevant structural analysis.
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Computational Cardiac Anatomy: 3D analysis of heart function
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In collaboration with Dr. Elliot McVeigh of the Johns Hopkins University, we are developing algorithms and tools for the quantitative analysis of myocardial function. Towards this, we research on myocardial motion and strain estimation, and population based strain statistics from tagged MRI datasets.
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Symmetric Large-Deformation Registration
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Medical image registration is the task of finding the topology-preserving transformation between two images, A and B, which brings them into correspondence. One problem with many current methods is that transformation depends on the ordering of the images. We have developed large-deformation registration tools [1] which are symmetric with respect to the images.
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Functional Magnetic Resonance Imaging Data Analysis
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The goal of this project is increasing the accuracy of fMRI statistical analysis through accurate group normalization. The collaborators are Dr. Lei Wang and Dr. Deanna Barch from Conte-Center, Washington University.
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Robust Cortical Thickness Measurement from MRI
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This project focuses on the development of robust computational tools for the measurement of cortical thickness. Cortical thickness is a measure of brain shape that has been found to change in some neurodegenerative diseases, including Alzheimer's Disease, AIDS, and Parkinson's Disease. Reliable thickness measurements may lead to techniques for early diagnosis of these diseases, as well as distinguishing between diseases with similar cognitive effects.
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Accurate Localization of MEG Functional Data Using Head Shape Registration
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We investigated two new techniques in which the we use external features of the head in subject-to-atlas registration, avoiding the acquisition of MR images per subject. The first method involves placing landmarks on a subject's head exterior and performing affine registration, and the second uses a non-linear fluid registration technique (LDDMM) on the external head-shape of a subject.
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FreeSurfer-Initiated Putamen, Cadate and Thalamus Segmentation in MRI Using Large Deformation Diffeomorphic Metric Mapping
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We describe a new algorithm for the automated segmentation of the caudate
(Caud) putamen (Put) and thalamus (Thal) in clinical Magnetic Resonance Imaging
(MRI) scans. Large Deformation Diffeomorphic Metric Mapping is performed on
Freesurfer-initiated templates to generate segmentation results. MR images of 24
brains (including Parkinson's diseased and Control) are used to test the algorithm.
The results are compared with manual segmentations under different measurements
of similarity.
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Artificial Life Approaches to Medical Image Analysis
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We are developing techniques for analysis of medical images based on modeling and utilizing knowledge about the underlying anatomy in the image. We are wokring on developing intelligent deformable models (deformable organisms) that live in the image space and whose goal is to locate and label anatomy.
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3D Shape Analysis and Visualization
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We are developing novel approaches and tools for the problem of the quantitative and qualitative analysis and visualization of 3D shapes. The aim is to apply these approaches to medical problems of anatomical shape analysis.
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Musculoskeletal Image Analysis
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We are developing tools for the analysis of medical imaging data for quantification, visualization, and understanding musculoskeletal anatomy and function and their relation to diseases.
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Computational Cardiac Anatomy
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We are utilizing canine cardiac DTMRI data to determine the biomechanical properties of the heart. We are developing new techniques for processing, smoothing, and analyzing this data.
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Multi-Modal Medical Image Registration
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We are working on non-rigidly registering multi-modal images, including nuclear medicine images to x-ray CT using mutual information and intensity correlation based similarity metrics.
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ITK Deformable Organisms Framework
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We are developing a new ITK-based Deformable Organisms framework. The framwork facilitaties the design of geometrical, dynamic, behavioral, and cognitive layers, and perception capabilities by making use of ITK classes and coding style.
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